Dissecting intratumoral myeloid cell plasticity by single cell RNA-seq

From monocyte-derived macrophages to resident macrophages-how metabolism leads their way in cancer

Macrophages are innate immune cells that play key roles during both homeostasis and disease. Depending on the microenvironmental cues sensed in different tissues, macrophages are known to acquire specific phenotypes and exhibit unique features that, ultimately, orchestrate tissue homeostasis, defense, and repair. Within the tumor microenvironment, macrophages are referred to as tumor-associated macrophages (TAMs) and constitute a heterogeneous population. Like their tissue resident counterpart, TAMs are plastic and can switch function and phenotype according to the niche-derived stimuli sensed. While changes in TAM phenotype are known to be accompanied by adaptive alterations in their cell metabolism, it is reported that metabolic reprogramming of macrophages can dictate their activation state and function. In line with these observations, recent research efforts have been focused on defining the metabolic traits of TAM subsets in different tumor malignancies and understanding their role in cancer progression and metastasis formation. This knowledge will pave the way to novel therapeutic strategies tailored to cancer subtype-specific metabolic landscapes. This review outlines the metabolic characteristics of distinct TAM subsets and their implications in tumorigenesis across multiple cancer types.

Hallmarks of tumor-experienced T cells are absent in multiple myeloma patients from diagnosis through maintenance therapy

Dysregulation of the bone marrow (BM) niche in multiple myeloma (MM) alters the composition and state of resident immune cells, potentially impeding anti-tumor immunity. One common mechanism of immune inhibition in solid tumors is the induction of exhaustion in tumor-specific T cells. However, the extent of T cell tumor recognition and exhaustion is not well-characterized in MM. As the specific mechanisms of immune evasion are critical for devising effective therapeutic strategies, we deeply profiled the CD8+ T cell compartment of newly-diagnosed MM (NDMM) patients for evidence of tumor reactivity and T cell exhaustion. We applied single-cell multi-omic sequencing and antigen-specific mass cytometry to longitudinal BM and peripheral blood (PB) samples taken from timepoints spanning from diagnosis through induction therapy, autologous stem cell transplant (ASCT), and maintenance therapy. We identified an exhausted-like population that lacked several canonical exhaustion markers, was not significantly enriched in NDMM patients, and consisted of small, nonpersistent clones. We also observed an activated population with increased frequency in the PB of NDMM patients exhibiting phenotypic and clonal features consistent with homeostatic, antigen-nonspecific activation. However, there was no evidence of "tumor-experienced" T cells displaying hallmarks of terminal exhaustion and/or tumor-specific activation/expansion in NDMM patients at any timepoint.

PERCEPTION predicts patient response and resistance to treatment using single-cell transcriptomics of their tumors

Tailoring optimal treatment for individual cancer patients remains a significant challenge. To address this issue, we developed PERCEPTION (PERsonalized Single-Cell Expression-Based Planning for Treatments In ONcology), a precision oncology computational pipeline. Our approach uses publicly available matched bulk and single-cell (sc) expression profiles from large-scale cell-line drug screens. These profiles help build treatment response models based on patients' sc-tumor transcriptomics. PERCEPTION demonstrates success in predicting responses to targeted therapies in cultured and patient-tumor-derived primary cells, as well as in two clinical trials for multiple myeloma and breast cancer. It also captures the resistance development in patients with lung cancer treated with tyrosine kinase inhibitors. PERCEPTION outperforms published state-of-the-art sc-based and bulk-based predictors in all clinical cohorts. PERCEPTION is accessible at https://github.com/ruppinlab/PERCEPTION . Our work, showcasing patient stratification using sc-expression profiles of their tumors, will encourage the adoption of sc-omics profiling in clinical settings, enhancing precision oncology tools based on sc-omics.

IFN-α/β/IFN-γ/IL-15 pathways identify GBP1-expressing tumors with an immune-responsive phenotype

Immunotherapy is widely used in cancer treatment; however, only a subset of patients responds well to it. Significant efforts have been made to identify patients who will benefit from immunotherapy. Successful anti-tumor immunity depends on an intact cancer-immunity cycle, especially long-lasting CD8+ T-cell responses. Interferon (IFN)-α/β/IFN-γ/interleukin (IL)-15 pathways have been reported to be involved in the development of CD8+ T cells. And these pathways may predict responses to immunotherapy. Herein, we aimed to analyze multiple public databases to investigate whether IFN-α/β/IFN-γ/IL-15 pathways could be used to predict the response to immunotherapy. Results showed that IFN-α/β/IFN-γ/IL-15 pathways could efficiently predict immunotherapy response, and guanylate-binding protein 1 (GBP1) could represent the IFN-α/β/IFN-γ/IL-15 pathways. In public and private cohorts, we further demonstrated that GBP1 could efficiently predict the response to immunotherapy. Functionally, GBP1 was mainly expressed in macrophages and strongly correlated with chemokines involved in T-cell migration. Therefore, our study comprehensively investigated the potential role of GBP1 in immunotherapy, which could serve as a novel biomarker for immunotherapy and a target for drug development.

Deciphering the heterogeneity dominated by tumor-associated macrophages for survival prognostication and prediction of immunotherapy response in lung adenocarcinoma

Tumor-associated macrophages (TAMs) are a specific subset of macrophages that reside inside the tumor microenvironment. The dynamic interplay between TAMs and tumor cells plays a crucial role in the treatment response and prognosis of lung adenocarcinoma (LUAD). The study aimed to examine the association between TAMs and LUAD to advance the development of targeted strategies and immunotherapeutic approaches for treating this type of lung cancer. The study employed single-cell mRNA sequencing data to characterize the immune cell composition of LUAD and delineate distinct subpopulations of TAMs. The "BayesPrism" and "Seurat" R packages were employed to examine the association between these subgroups and immunotherapy and clinical features to identify novel immunotherapy biomarkers. Furthermore, a predictive signature was generated to forecast patient prognosis by examining the gene expression profile of immunotherapy-associated TAMs subsets and using 104 machine-learning techniques. A comprehensive investigation has shown the existence of a hitherto unidentified subgroup of TAMs known as RGS1 + TAMs, which has been found to have a strong correlation with the efficacy of immunotherapy and the occurrence of tumor metastasis in LUAD patients. CD83 was identified CD83 as a distinct biomarker for the expression of RGS1 + TAMs, showcasing its potential utility as an indicator for immunotherapeutic interventions. Furthermore, the prognostic capacity of the RTMscore signature, encompassing three specific mRNA (NR4A2, MMP14, and NPC2), demonstrated enhanced robustness when contrasted against the comprehensive collection of 104 features outlined in the published study. CD83 has potential as an immunotherapeutic biomarker. Meanwhile, The RTMscore signature established in the present study might be beneficial for survival prognostication.

An integrative pan-cancer analysis of the molecular characteristics of dietary restriction in tumour microenvironment

BACKGROUND

Dietary restriction (DR), a general term for dieting, has been demonstrated as an effective intervention in reducing the occurrence of cancers. Molecular activities associated with DR are crucial in mediating its anti-cancer effects, yet a comprehensive exploration of the landscape of these activities at the pan-cancer level is still lacking.

METHODS

We proposed a computational approach for quantifying DR-related molecular activities and delineating the landscape of these activities across 33 cancer types and 30 normal tissues within 27,320 samples. We thoroughly examined the associations between DR-related molecular activities and various factors, including the tumour microenvironment, immunological phenotypes, genomic features, and clinical prognosis. Meanwhile, we identified two DR genes that show potential as prognostic predictors in hepatocellular carcinoma and verified them by immunohistochemical assays in 90 patients.

FINDINGS

We found that DR-related molecular activities showed a close association with tumour immunity and hold potential for predicting immunotherapy responses in various cancers. Importantly, a higher level of DR-related molecular activities is associated with improved overall survival and cancer-specific survival. FZD1 and G6PD are two DR genes that serve as biomarkers for predicting the prognosis of patients with hepatocellular carcinoma.

INTERPRETATION

This study presents a robust link between DR-related molecular activities and tumour immunity across multiple cancer types. Our research could open the path for further investigation of DR-related molecular processes in cancer treatment.

FUNDING

National Natural Science Foundation of China (Grant No. 82000628) and the Guangdong-Hong Kong-Macao University Joint Laboratory of Interventional Medicine Foundation of Guangdong Province (Grant No. 2023LSYS001).

Analysis of the effect of CCR7 on the microenvironment of mouse oral squamous cell carcinoma by single-cell RNA sequencing technology

BACKGROUND

Studies have shown that CCR7, an important inflammatory factor, can promote the proliferation and metastasis of oral squamous cell carcinoma (OSCC), but its role in the tumor microenvironment (TME) remains unclear. This paper explores the role of CCR7 in the TME of OSCC.

METHODS

In this work, we constructed CCR7 gene knockout mice and OSCC mouse models. Single-cell RNA sequencing (scRNA-seq) and bioinformatics were used to analyze the differences in the OSCC microenvironment between three CCR7 gene knockout mice (KO) and three wild-type mice (WT). Immunohistochemistry, immunofluorescence staining, and flow cytometry were used to analyze the expression of key genes in significantly different cell types between the KO and WT groups. An in vitro experiment was used to verify the effect of CCR7 on M2 macrophage polarization.

RESULTS

In the mouse OSCC models, the tumor growth rate in the KO group was significantly lower than that in the WT group. Eight main cell types (including tumor cells, fibroblasts, macrophages, granulocytes, T cells, endothelial cells, monocytes, and B cells) were identified by Seurat analysis. The scRNA-seq results showed that the proportion of tumor cells was lower, but the proportion of inflammatory cells was significantly higher in the KO group than in the WT group. CellPhoneDB analysis results indicated a strong interaction relationship between tumor cells and macrophages, T cells, fibroblasts, and endothelial cells. Functional enrichment results indicated that the expression level of the Dusp1 gene in the KO group was generally higher than that in the WT group in various cell types. Macrophage subclustering results indicated that the proportion of M2 macrophages in the KO group was lower than that in the WT group. In vitro experimental results showed that CCR7 can promote M2 macrophage polarization, thus promoting the proliferation, invasion and migration of OSCC cells.

CONCLUSIONS

CCR7 gene knockout can significantly inhibit the growth of mouse oral squamous cell carcinoma by promoting the polarization of M2 macrophages.

The landscape of immune checkpoint-related long non-coding RNAs core regulatory circuitry reveals implications for immunoregulation and immunotherapy responses

Long non-coding RNAs (lncRNAs) could modulate expression of immune checkpoints (ICPs) by cooperating with immunity genes in tumor immunization. However, precise functions in immunity and potential for predicting ICP inhibitors (ICI) response have been described for only a few lncRNAs. Here we present an integrated framework that leverages network-based analyses and Bayesian network inference to identify the regulated relationships including lncRNA, ICP and immunity genes as ICP-related LncRNAs mediated Core Regulatory Circuitry Triplets (ICP-LncCRCTs) that can make robust predictions. Hub ICP-related lncRNAs such as MIR155HG and ADAMTS9-AS2 were highlighted to play central roles in immune regulation. Specific ICP-related lncRNAs could distinguish cancer subtypes. Moreover, the ICP-related lncRNAs are likely to significantly correlated with immune cell infiltration, MHC, CYT. Some ICP-LncCRCTs such as CXCL10-MIR155HG-ICOS could better predict one-, three- and five-year prognosis compared to single molecule in melanoma. We also validated that some ICP-LncCRCTs could effectively predict ICI-response using three kinds of machine learning algorithms follow five independent datasets. Specially, combining ICP-LncCRCTs with the tumor mutation burden (TMB) improves the prediction of ICI-treated melanoma patients. Altogether, this study will improve our grasp of lncRNA functions and accelerating discovery of lncRNA-based biomarkers in ICI treatment.

MCL1 inhibition targets Myeloid Derived Suppressors Cells, promotes antitumor immunity and enhances the efficacy of immune checkpoint blockade

Immune checkpoint inhibitors (ICIs) are now the first-line treatment for patients with advanced melanoma. Despite promising clinical results, many patients fail to respond to these therapies. BH3 mimetics, a novel class of small molecule inhibitors that bind and inhibit anti-apoptotic members of the BCL2 family proteins such as BCL2 or MCL1, have been very successful in treating hematologic malignancies. However, there are limited studies on the immunomodulatory role of the BH3 mimetics. Several factors contribute to ICI resistance including myeloid-derived suppressor cells (MDSCs) that exert immunosuppressive effects through direct and indirect inhibition of antitumor immunity. Thus, targeting MDSCs to enhance antitumor immunity has the potential to enhance the efficacy of ICIs. In this study, we show that the MCL1 inhibitor S64315 reduces melanoma tumor growth in an immune cell-dependent manner in mice. Specifically, S64315 enhances antitumor immunity by reducing MDSC frequency and by promoting the activity of CD8+T cells. Additionally, human MDSCs are 10 times more sensitive to S64315 than cutaneous melanoma lines. Further, we found that a higher expression of MCL1 is associated with poor survival for patients treated with anti-PD-1. Finally, combining S64315 and anti-PD-1 significantly slowed tumor growth compared to either agent alone. Together, this proof-of-concept study demonstrates the potential of combining an MCL1 inhibitor with anti-PD-1 in the treatment of melanoma. It justifies the further development of next generation MCL1 inhibitors to improve efficacy of ICIs in treating malignant melanoma.

Multi-omics analysis of disulfidptosis regulators and therapeutic potential reveals glycogen synthase 1 as a disulfidptosis triggering target for triple-negative breast cancer

Disruption of disulfide homeostasis during biological processes can have fatal consequences. Excess disulfides induce cell death in a novel manner, termed as "disulfidptosis." However, the specific mechanism of disulfidptosis has not yet been elucidated. To determine the cancer types sensitive to disulfidptosis and outline the corresponding treatment strategies, we firstly investigated the crucial functions of disulfidptosis regulators pan-cancer at multi-omics levels. We found that different tumor types expressed dysregulated levels of disulfidptosis regulators, most of which had an impact on tumor prognosis. Moreover, we calculated the disulfidptosis activity score in tumors and validated it using multiple independent datasets. Additionally, we found that disulfidptosis activity was correlated with classic biological processes and pathways in various cancers. Disulfidptosis activity was also associated with tumor immune characteristics and could predict immunotherapy outcomes. Notably, the disulfidptosis regulator, glycogen synthase 1 (GYS1), was identified as a promising target for triple-negative breast cancer and validated via in vitro and in vivo experiments. In conclusion, our study elucidated the complex molecular phenotypes and clinicopathological correlations of disulfidptosis regulators in tumors, laying a solid foundation for the development of disulfidptosis-targeting strategies for cancer treatment.

Advanced insights on tumor-associated macrophages revealed by single-cell RNA sequencing: The intratumor heterogeneity, functional phenotypes, and cellular interactions

Single-cell RNA sequencing (scRNA-seq) is an emerging technology used for cellular transcriptome analysis. The application of scRNA-seq has led to profoundly advanced oncology research, continuously optimizing novel therapeutic strategies. Intratumor heterogeneity extensively consists of all tumor components, contributing to different tumor behaviors and treatment responses. Tumor-associated macrophages (TAMs), the core immune cells linking innate and adaptive immunity, play significant roles in tumor progression and resistance to therapies. Moreover, dynamic changes occur in TAM phenotypes and functions subject to the regulation of the tumor microenvironment. The heterogeneity of TAMs corresponding to the state of the tumor microenvironment has been comprehensively recognized using scRNA-seq. Herein, we reviewed recent research and summarized variations in TAM phenotypes and functions from a developmental perspective to better understand the significance of TAMs in the tumor microenvironment.

RSK4 promotes the macrophage recruitment and M2 polarization in esophageal squamous cell carcinoma

High infiltration of tumor-associated macrophages (TAMs) participates in host immunity and tumor progression in patients with esophageal squamous cell carcinoma (ESCC). Ribosomal s6 kinase 4 (RSK4) has been shown to be aberrantly overexpressed in ESCC. The role of RSK4 in cytokine secretion and its impact on macrophage recruitment and M2 polarization remains unclear. Therefore, a thorough understanding of RSK4 is needed to expand our knowledge of its therapeutic potential. Herein, RSK4 expression in human ESCC tissues and a xenograft mouse model was positively correlated with high infiltration of M0 and M2 macrophages which is positively associated with unfavorable overall survival outcomes and treatment resistance in patients with ESCC. In vitro experiments revealed that RSK4 derived from ESCC cells promoted macrophage recruitment and M2 polarization by enhancingsoluble intercellular adhesion molecule-1 (sICAM-1) secretion via direct and indirect STAT3 phosphorylation. Furthermore, RSK4-induced macrophages enhanced tumor proliferation, migration, and invasion by secreting C-C motif chemokine ligand 22 (CCL22). We further showed that patients with elevated CD68 and CD206 expression had unfavorable overall survival. Collectively, these results demonstrate that RSK4 promotes the macrophage recruitment and M2 polarization by regulating the STAT3/ICAM-1 axis in ESCC, influencing tumor progression primarily in a CCL22-dependent manner. These data also offer valuable insights for developing novel agents for the treatment of ESCC.

MerTK-dependent efferocytosis by monocytic-MDSCs mediates resolution of post-lung transplant injury

Rationale

Patients with end stage lung diseases require lung transplantation (LTx) that can be impeded by ischemia-reperfusion injury (IRI) leading to subsequent chronic lung allograft dysfunction (CLAD) and inadequate outcomes.

Objectives

We examined the undefined role of MerTK (receptor Mer tyrosine kinase) on monocytic myeloid-derived suppressor cells (M-MDSCs) in efferocytosis (phagocytosis of apoptotic cells) to facilitate resolution of lung IRI.

Methods

Single-cell RNA sequencing of lung tissue and BAL from post-LTx patients was analyzed. Murine lung hilar ligation and allogeneic orthotopic LTx models of IRI were used with Balb/c (WT), cebpb -/- (MDSC-deficient), Mertk -/- or MerTK-CR (cleavage resistant) mice. Lung function, IRI (inflammatory cytokine and myeloperoxidase expression, immunohistology for neutrophil infiltration), and flow cytometry of lung tissue for efferocytosis of apoptotic neutrophils were assessed in mice.

Measurements and Main Results

A significant downregulation in MerTK-related efferocytosis genes in M-MDSC populations of CLAD patients compared to healthy subjects was observed. In the murine IRI model, significant increase in M-MDSCs, MerTK expression and efferocytosis was observed in WT mice during resolution phase that was absent in cebpb -/- Land Mertk -/- mice. Adoptive transfer of M-MDSCs in cebpb -/- mice significantly attenuated lung dysfunction, and inflammation leading to resolution of IRI. Additionally, in a preclinical murine orthotopic LTx model, increases in M-MDSCs were associated with resolution of lung IRI in the transplant recipients. In vitro studies demonstrated the ability of M-MDSCs to efferocytose apoptotic neutrophils in a MerTK-dependent manner.

Conclusions

Our results suggest that MerTK-dependent efferocytosis by M-MDSCs can significantly contribute to the resolution of post-LTx IRI.

T proliferating cells derived autophagy signature associated with prognosis and immunotherapy resistance in a pan-cancer analysis

Despite autophagy modulating tumor immunity in the tumor microenvironment (TME), the immunotherapeutic efficacy and potential mechanism of autophagy signature was not explicit. We manually curated an autophagy gene set and defined a pan-cancer autophagy signature by comparing malignant tissues and normal tissues in The Cancer Genome Atlas (TCGA) cohort. The pan-cancer autophagy signature was derived from T proliferating cells as demonstrated in multiple single-cell RNA sequencing (scRNA-seq) datasets. The pan-cancer autophagy signature could influence the cell-cell interactions in the TME and predict the responsiveness of immune checkpoint inhibitors (ICIs) in the metastatic renal cell carcinoma, non-small cell lung cancer, bladder cancer, and melanoma cohorts. Metabolism inactivation accompanied with dysregulation of autophagy was investigated with transcriptomic and proteomic data. The immunotherapeutic predictive role and mechanism regulation of the autophagy signature was validated in an in-house cohort. Our study provides valuable insights into the mechanisms of ICI resistance.

Identification of prognostic value of anoikis-related gene score model combined with tumor microenvironment score models in esophageal squamous cell carcinoma

BACKGROUND

Esophageal squamous cell carcinoma (ESCC) has poor survival. Effective prognostic models with high application value remain lack.

METHODS

Bulk RNA seq and single cell RNA-seq data were retrieved from the XENA-TCGA-ESCC cohort and GSE188900. The anoikis-related gene score (ANO score) model and tumor microenvironment score (TME score) model were constructed and merged into three subgroups. Functional annotation was analyzed by Gene Ontology terms. Univariate and multivariate Cox regression analysis, least absolute shrinkage and selection operator regression analysis and weighted gene coexpression network analysis were performed to construct prognostic prediction models and identify prognostic value. Kaplan-Meier survival curves were drawn for evaluating the overall survival (OS) of patients classified by different score subgroups. Immunotherapy response and mutation analyses were also conducted.

RESULTS

In the ANO score model, TNFSF10 was an independent factor for the prognosis of ESCC patients. The area under the curve values of the ANO-TME score model in predicting the OS were 0.638 at 5 years and 0.632 at 7 years. Patients in the ANO low score-TME high score group had a much longer OS than patients in any other ANO-TME score subgroup (p < 0.001), suggesting a higher prognostic value. The differentially expressed genes of the ANO low score-TME high score group were mainly involved in cell adhesion molecules, nucleotide excision repair, the TGF-β signaling pathway and mismatch repair. TP53 (92%), TTN (38%) and NFE2L2 (31%) were the top genes with highest mutant frequency in the ANO low score-TME high score group.

CONCLUSIONS

A novel prognostic prediction model with high application value was constructed and identified for ESCC patients, which may provide evidence for immunotherapy in the treatment of ESCC.

Anoikis-related CTNND1 is associated with immuno-suppressive tumor microenvironment and predicts unfavorable immunotherapeutic outcome in non-small cell lung cancer

Background: Immunotherapy has greatly changed the treatment of advanced non-small cell lung cancer (NSCLC). Anoikis is a programmed cell death process associated with cancer. However, the correlation between anoikis-related genes and the tumor microenvironment (TME) features and immunotherapeutic outcome in NSCLC has not been fully explored. Methods: The bulk and single-cell transcriptome data of NSCLC were downloaded from TCGA and GEO databases. The distribution of anoikis-related genes on different cell types at the single-cell level was analyzed, and these genes specifically expressed by tumor cells and immunotherapy-related were further extracted. Next, the candidate gene CTNND1 was identified and its correlations with the TME features and immunotherapeutic outcome in NSCLC were explored in multiple public cohorts. Finally, an in-house cohort was used to determine the CTNND1 expression and immuno-correlation in NSCLC. Results: At single-cell atlas, we found that anoikis-related genes expressed specifically in tumor cells of NSCLC. By intersecting anoikis-related genes, immunotherapy-associated genes, and the genes expressed in tumor cells, we obtained a special biomarker CTNND1. In addition, cell-cell communication analysis revealed that CTNND1+ tumor cells communicated with immune subpopulations frequently. Moreover, we found that high expression of CTNND1 was related to immuno-suppressive status of NSCLC. The expression of CTNND1 and its immuno-correlation were also validated, and the results showed that CTNND1 was highly expressed in NSCLC tissues and tumors with high CTNND1 expression accompanied with low CD8+ T cells infiltration. Conclusions: Overall, our study reported that CTNND1 can be considered as a novel biomarker for the predication of immunotherapeutic responses and a potential target for NSCLC therapy.

Tumor aerobic glycolysis confers immune evasion through modulating sensitivity to T cell-mediated bystander killing via TNF-α

Metabolic reprogramming toward glycolysis is a hallmark of cancer malignancy. The molecular mechanisms by which the tumor glycolysis pathway promotes immune evasion remain to be elucidated. Here, by performing genome-wide CRISPR screens in murine tumor cells co-cultured with cytotoxic T cells (CTLs), we identified that deficiency of two important glycolysis enzymes, Glut1 (glucose transporter 1) and Gpi1 (glucose-6-phosphate isomerase 1), resulted in enhanced killing of tumor cells by CTLs. Mechanistically, Glut1 inactivation causes metabolic rewiring toward oxidative phosphorylation, which generates an excessive amount of reactive oxygen species (ROS). Accumulated ROS potentiate tumor cell death mediated by tumor necrosis factor alpha (TNF-α) in a caspase-8- and Fadd-dependent manner. Genetic and pharmacological inactivation of Glut1 sensitizes tumors to anti-tumor immunity and synergizes with anti-PD-1 therapy through the TNF-α pathway. The mechanistic interplay between tumor-intrinsic glycolysis and TNF-α-induced killing provides new therapeutic strategies to enhance anti-tumor immunity.

Application of single-cell RNA sequencing in head and neck squamous cell carcinoma

Single-cell RNA sequencing has been broadly applied to head and neck squamous cell carcinoma (HNSCC) for characterizing the heterogeneity and genomic mutations of HNSCC benefiting from the advantage of single-cell resolution. We summarized most of the current studies and aimed to explore their research methods and ideas, as well as how to transform them into clinical applications. Through single-cell RNA sequencing, we found the differences in tumor cells' expression programs and differentiation tracks. The studies of immune microenvironment allowed us to distinguish immune cell subpopulations, the extensive expression of immune checkpoints, and the complex crosstalk network between immune cells and non-immune cells. For cancer-associated fibroblasts (CAFs), single-cell RNA sequencing had made an irreplaceable contribution to the exploration of their differentiation status, specific CAFs markers, and the interaction with tumor cells and immune cells. In addition, we demonstrated in detail how single-cell RNA sequencing explored the HNSCC epithelial-to-mesenchymal transition (EMT) model and the mechanism of drug resistance, as well as its clinical value.

The miR-23-27-24 clusters drive lipid-associated macrophage proliferation in obese adipose tissue

Identifying molecular circuits that control adipose tissue macrophage (ATM) function is necessary to understand how ATMs contribute to tissue homeostasis and obesity-induced insulin resistance. In this study, we find that mice with a myeloid-specific knockout of the miR-23-27-24 clusters of microRNAs (miRNAs) gain less weight on a high-fat diet but exhibit worsened glucose and insulin tolerance. Analysis of ATMs from these mice shows selectively reduced numbers and proliferation of a recently reported subset of lipid-associated CD9+Trem2+ ATMs (lipid-associated macrophages [LAMs]). Leveraging the role of miRNAs to control networks of genes, we use RNA sequencing (RNA-seq), functional screens, and biochemical assays to identify candidate target transcripts that regulate proliferation-associated signaling. We determine that miR-23 directly targets the mRNA of Eif4ebp2, a gene that restricts protein synthesis and proliferation in macrophages. Altogether, our study demonstrates that control of proliferation of a protective subset of LAMs by noncoding RNAs contributes to protection against diet-induced obesity metabolic dysfunction.

scGEM: Unveiling the Nested Tree-Structured Gene Co-Expressing Modules in Single Cell Transcriptome Data

BACKGROUND

Single-cell transcriptome analysis has fundamentally changed biological research by allowing higher-resolution computational analysis of individual cells and subsets of cell types. However, few methods have met the need to recognize and quantify the underlying cellular programs that determine the specialization and differentiation of the cell types.

METHODS

In this study, we present scGEM, a nested tree-structured nonparametric Bayesian model, to reveal the gene co-expression modules (GEMs) reflecting transcriptome processes in single cells.

RESULTS

We show that scGEM can discover shared and specialized transcriptome signals across different cell types using peripheral blood mononuclear single cells and early brain development single cells. scGEM outperformed other methods in perplexity and topic coherence (p < 0.001) on our simulation data. Larger datasets, deeper trees and pre-trained models are shown to be positively associated with better scGEM performance. The GEMs obtained from triple-negative breast cancer single cells exhibited better correlations with lymphocyte infiltration (p = 0.009) and the cell cycle (p < 0.001) than other methods in additional validation on the bulk RNAseq dataset.

CONCLUSIONS

Altogether, we demonstrate that scGEM can be used to model the hidden cellular functions of single cells, thereby unveiling the specialization and generalization of transcriptomic programs across different types of cells.

Bioinformatics analysis and single-cell RNA sequencing: elucidating the ubiquitination pathways and key enzymes in lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) is a prevalent subtype of lung cancer associated with high mortality rates. We aimed to utilize single-cell multiomics analysis to identify the key molecules involved in ubiquitination modification, which plays a role in LUAD development and progression.

METHODS

We use a systematic approach to analyze LUAD-related single-cell and bulk transcriptome datasets from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Single-cell RNA sequencing (scRNA-seq) data were normalized, clustered, and annotated with the Seurat package in R. InferCNV was used to distinguish malignant from epithelial cells, and AUCell evaluated the area under the curve (AUC) score of ubiquitination-related enzymes. Survival and differential analyses identified significant molecular markers associated with ubiquitination. PSMD14 expression was confirmed using reverse-transcription quantitative polymerase chain reaction (RT-qPCR) and Western blot assays, and its knockdown cell lines were assessed for effects on cellular processes and tumor formation in mice. PSMD14's interacting proteins were predicted, and its impact on AGR2 protein half-life and ubiquitination was evaluated. Rescue experiments involving PSMD14 overexpression and AGR2 silencing assessed their impact on malignant behaviors.

RESULTS

By means of single-cell sequencing analysis, we probed the ubiquitination modification landscape in the LUAD microenvironment. Malignant cells had elevated scores for enzymes and ubiquitin-binding domains compared to normal epithelial cells, with 53 ubiquitination-related molecules showing prognostic disparities. FGR, PSMD14, and ZBTB16 were identified as genes with prognostic significance, with PSMD14 showing higher expression in epithelial and malignant cells. Two missense mutation sites were identified in PSMD14, which had a high copy number amplification ratio and positive correlation with messenger RNA (mRNA) expression. PSMD14 expression and tumor stage were found to be independent prognostic factors, and interfering with PSMD14 expression reduced the malignant behavior of LUAD cells. PSMD14 was found to bind to AGR2 protein and reduce its ubiquitination, leading to increased AGR2 stability. Knockdown of AGR2 inhibited the enhancement of cell viability, invasion, and migration resulting from PSMD14 overexpression.

CONCLUSIONS

This study examined ubiquitination modifications in LUAD using sequencing data, identifying PSMD14's critical role in malignancy regulation and its potential as a prognostic and therapeutic biomarker. These insights enhance understanding of LUAD mechanisms and treatment.

Single-cell sequencing reveals the landscape of the human brain metastatic microenvironment

Brain metastases is the most common intracranial tumor and account for approximately 20% of all systematic cancer cases. It is a leading cause of death in advanced-stage cancer, resulting in a five-year overall survival rate below 10%. Therefore, there is a critical need to identify effective biomarkers that can support frequent surveillance and promote efficient drug guidance in brain metastasis. Recently, the remarkable breakthroughs in single-cell RNA-sequencing (scRNA-seq) technology have advanced our insights into the tumor microenvironment (TME) at single-cell resolution, which offers the potential to unravel the metastasis-related cellular crosstalk and provides the potential for improving therapeutic effects mediated by multifaceted cellular interactions within TME. In this study, we have applied scRNA-seq and profiled 10,896 cells collected from five brain tumor tissue samples originating from breast and lung cancers. Our analysis reveals the presence of various intratumoral components, including tumor cells, fibroblasts, myeloid cells, stromal cells expressing neural stem cell markers, as well as minor populations of oligodendrocytes and T cells. Interestingly, distinct cellular compositions are observed across different samples, indicating the influence of diverse cellular interactions on the infiltration patterns within the TME. Importantly, we identify tumor-associated fibroblasts in both our in-house dataset and external scRNA-seq datasets. These fibroblasts exhibit high expression of type I collagen genes, dominate cell-cell interactions within the TME via the type I collagen signaling axis, and facilitate the remodeling of the TME to a collagen-I-rich extracellular matrix similar to the original TME at primary sites. Additionally, we observe M1 activation in native microglial cells and infiltrated macrophages, which may contribute to a proinflammatory TME and the upregulation of collagen type I expression in fibroblasts. Furthermore, tumor cell-specific receptors exhibit a significant association with patient survival in both brain metastasis and native glioblastoma cases. Taken together, our comprehensive analyses identify type I collagen-secreting tumor-associated fibroblasts as key mediators in metastatic brain tumors and uncover tumor receptors that are potentially associated with patient survival. These discoveries provide potential biomarkers for effective therapeutic targets and intervention strategies.

Transfer learning enables predictions in network biology

Mapping gene networks requires large amounts of transcriptomic data to learn the connections between genes, which impedes discoveries in settings with limited data, including rare diseases and diseases affecting clinically inaccessible tissues. Recently, transfer learning has revolutionized fields such as natural language understanding1,2 and computer vision3 by leveraging deep learning models pretrained on large-scale general datasets that can then be fine-tuned towards a vast array of downstream tasks with limited task-specific data. Here, we developed a context-aware, attention-based deep learning model, Geneformer, pretrained on a large-scale corpus of about 30 million single-cell transcriptomes to enable context-specific predictions in settings with limited data in network biology. During pretraining, Geneformer gained a fundamental understanding of network dynamics, encoding network hierarchy in the attention weights of the model in a completely self-supervised manner. Fine-tuning towards a diverse panel of downstream tasks relevant to chromatin and network dynamics using limited task-specific data demonstrated that Geneformer consistently boosted predictive accuracy. Applied to disease modelling with limited patient data, Geneformer identified candidate therapeutic targets for cardiomyopathy. Overall, Geneformer represents a pretrained deep learning model from which fine-tuning towards a broad range of downstream applications can be pursued to accelerate discovery of key network regulators and candidate therapeutic targets.

Discovery of Targets for Immune-Metabolic Antitumor Drugs Identifies Estrogen-Related Receptor Alpha

Drugs that kill tumors through multiple mechanisms have the potential for broad clinical benefits. Here, we first developed an in silico multiomics approach (BipotentR) to find cancer cell-specific regulators that simultaneously modulate tumor immunity and another oncogenic pathway and then used it to identify 38 candidate immune-metabolic regulators. We show the tumor activities of these regulators stratify patients with melanoma by their response to anti-PD-1 using machine learning and deep neural approaches, which improve the predictive power of current biomarkers. The topmost identified regulator, ESRRA, is activated in immunotherapy-resistant tumors. Its inhibition killed tumors by suppressing energy metabolism and activating two immune mechanisms: (i) cytokine induction, causing proinflammatory macrophage polarization, and (ii) antigen-presentation stimulation, recruiting CD8+ T cells into tumors. We also demonstrate a wide utility of BipotentR by applying it to angiogenesis and growth suppressor evasion pathways. BipotentR (http://bipotentr.dfci.harvard.edu/) provides a resource for evaluating patient response and discovering drug targets that act simultaneously through multiple mechanisms.

SIGNIFICANCE

BipotentR presents resources for evaluating patient response and identifying targets for drugs that can kill tumors through multiple mechanisms concurrently. Inhibition of the topmost candidate target killed tumors by suppressing energy metabolism and effects on two immune mechanisms. This article is highlighted in the In This Issue feature, p. 517.

The Recurrent-Specific Regulation Network of Prognostic Stemness-Related Signatures in Low-Grade Glioma

Gliomas including astrocytomas, oligodendrogliomas, mixed oligoastrocytic, and mixed glioneuronal tumors are an important group of brain tumors. Based on the 2016 WHO classification for tumors in the central nervous system, gliomas were classified into four grades, from I to IV, and brain lower grade glioma (LGG) consists of grade II and grade III. Patients with LGG may undergo recurrence, which makes clinical treatment tough. Stem cell-like features of cancer cells play a key role in tumor's biological behaviors, including tumorigenesis, development, and clinical prognosis. In this article, we quantified the stemness feature of cancer cells using the mRNA stemness index (mRNAsi) and identified stemness-related key genes based on correlation with mRNAsi. Besides, hallmark gene sets and translate factors (TFs) which were highly related to stemness-related key genes were identified. Therefore, a recurrency-specific network was constructed and a potential regulation pathway was identified. Several online databases, assay for transposase-accessible chromatin using sequencing (ATAC-seq), single-cell sequencing analysis, and immunohistochemistry were utilized to validate the scientific hypothesis. Finally, we proposed that aurora kinase A (AURKA), positively regulated by Non-SMC Condensin I Complex Subunit G (NCAPG), promoted E2F target pathway in LGG, which played an important role in LGG recurrence.

Platinum-Based TREM2 Inhibitor Suppresses Tumors by Remodeling the Immunosuppressive Microenvironment

Triggering receptor expressed on myeloid cells-2 (TREM2) is a key pro-tumorigenic marker of tumor-infiltrating macrophages, showing potent immunosuppressive activity in tumor microenvironment. A platinum(IV) complex OPA derived from oxaliplatin (OP) and artesunate (ART) exhibited direct cytotoxicity against human colon cancer cells and immunomodulatory activity to inhibit TREM2 on macrophages in vitro and vivo. Furthermore, OPA deterred the tumor growth in mouse models bearing MC38 colorectal tumor by reducing the number of CD206⁺ and CX₃ CR1⁺ immunosuppressive macrophages; it also promoted the expansion and infiltration of immunostimulatory dendritic, cytotoxic T, and natural killer cells. OPA is the first small-molecular TREM2 inhibitor capable of relieving immunosuppressive tumor microenvironment and enhancing chemical anticancer efficiency of a platinum drug, thus showing typical characteristics of a chemoimmunotherapeutic agent.

VISTA Targeting of T-cell Quiescence and Myeloid Suppression Overcomes Adaptive Resistance

V domain immunoglobulin suppressor of T-cell activation (VISTA) is a premier target for cancer treatment due to its broad expression in many cancer types and enhanced expression upon development of adaptive immune checkpoint resistance. In the CT26 colorectal cancer model, monotherapy of small tumors with anti-VISTA resulted in slowed tumor growth. In a combination therapy setting, large CT26 tumors showed complete adaptive resistance to anti-PD-1/CTLA-4, but inclusion of anti-VISTA led to rejection of half the tumors. Mechanisms of enhanced antitumor immunity were investigated using single-cell RNA sequencing (scRNA-seq), multiplex image analysis, and flow cytometry of the tumor immune infiltrate. In both treatment models, anti-VISTA upregulated stimulated antigen presentation pathways and reduced myeloid-mediated suppression. Imaging revealed an anti-VISTA stimulated increase in contacts between T cells and myeloid cells, further supporting the notion of increased antigen presentation. scRNA-seq of tumor-specific CD8+ T cells revealed that anti-VISTA therapy induced T-cell pathways highly distinct from and complementary to those induced by anti-PD-1 therapy. Whereas anti-CTLA-4/PD-1 expanded progenitor exhausted CD8+ T-cell subsets, anti-VISTA promoted costimulatory genes and reduced regulators of T-cell quiescence. Notably, this is the first report of a checkpoint regulator impacting CD8+ T-cell quiescence, and the first indication that quiescence may be a target in the context of T-cell exhaustion and in cancer. This study builds a foundation for all future studies on the role of anti-VISTA in the development of antitumor immunity and provides important mechanistic insights that strongly support use of anti-VISTA to overcome the adaptive resistance seen in contemporary treatments involving PD-1 and/or CTLA-4. See related Spotlight by Wei, p. 3.

Phenotypes and Functions of Human Dendritic Cell Subsets in the Tumor Microenvironment

Dendritic cells (DCs) play a key role in the antitumor immunity, as they are at the interface of innate and adaptive immunity. This important task can only be performed thanks to the broad range of mechanisms that DCs can perform to activate other immune cells. As DCs are well known for their outstanding capacity to prime and activate T cells through antigen presentation, DCs were intensively investigated during the past decades. Numerous studies have identified new DC subsets, leading to a large variety of subsets commonly separated into cDC1, cDC2, pDCs, mature DCs, Langerhans cells, monocyte-derived DCs, Axl-DCs, and several other subsets. Here, we review the specific phenotypes, functions, and localization within the tumor microenvironment (TME) of human DC subsets thanks to flow cytometry and immunofluorescence but also with the help of high-output technologies such as single-cell RNA sequencing and imaging mass cytometry (IMC).

Myeloid checkpoints for cancer immunotherapy

Myeloid checkpoints are receptors on the myeloid cell surface which can mediate inhibitory signals to modulate anti-tumor immune activities. They can either inhibit cellular phagocytosis or suppress T cells and are thus involved in the pathogenesis of various diseases. In the tumor microenvironment, besides killing tumor cells by phagocytosis or activating anti-tumor immunity by tumor antigen presentation, myeloid cells could execute pro-tumor efficacies through myeloid checkpoints by interacting with counter-receptors on other immune cells or cancer cells. In summary, myeloid checkpoints may be promising therapeutic targets for cancer immunotherapy.

Decoding Single-cell Landscape and Intercellular Crosstalk in the Transplanted Liver

BACKGROUND

Liver transplantation (LT) is the most effective treatment for various end-stage liver diseases. However, the cellular complexity and intercellular crosstalk of the transplanted liver have constrained analyses of graft reconstruction after LT.

METHODS

We established an immune-tolerated orthotopic LT mouse model to understand the physiological process of graft recovery and intercellular crosstalk. We employed single-cell RNA sequencing and cytometry by time-of-flight to comprehensively reveal the cellular landscape.

RESULTS

We identified an acute and stable phase during perioperative graft recovery. Using single-cell technology, we made detailed annotations of the cellular landscape of the transplanted liver and determined dynamic modifications of these cells during LT. We found that 96% of graft-derived immune cells were replaced by recipient-derived cells from the preoperative to the stable phase. However, CD206 + MerTK + macrophages and CD49a + CD49b - natural killer cells were composed of both graft and recipient sources even in the stable phase. Intriguingly, the transcriptional profiles of these populations exhibited tissue-resident characteristics, suggesting that recipient-derived macrophages and natural killer cells have the potential to differentiate into 'tissue-resident cells' after LT. Furthermore, we described the transcriptional characteristics of these populations and implicated their role in regulating the metabolic and immune remodeling of the transplanted liver.

CONCLUSIONS

In summary, this study delineated a cell atlas (type-proportion-source-time) of the transplanted liver and shed light on the physiological process of graft reconstruction and graft-recipient crosstalk.

Single-cell RNA sequencing analysis revealed cellular and molecular immune profiles in lung squamous cell carcinoma

Although breakthroughs have been made in the treatment of non-small cell lung cancer, there are only a few choices for advanced-stage or recurrent lung squamous cell carcinoma (LUSC) patients. In our study, we identified 7 major cell types in thedepicted the immunolandscape of LUSC microenvironment using single-cell RNA sequencing. We found that an immunosuppressive receptor, T cell immunoglobulin and immunoreceptor tyrosine-based inhibitory motif domain (TIGIT), was highly expressed by regulatory T cells (Tregs) and exhausted CD8⁺T cells, suggesting that upregulation of TIGIT might promote an immunosuppressive microenvironment and inhibit the cytotoxic ability of CD8⁺T cells. We also identified tumor-associated neutrophil (TAN), characterized by CXCR2, CSF3R and CXCL8, in the tumor region, and TANs upregulated the expression of interleukin 1 receptor antagonist (IL1RN) which suggested that TAN might exert an immunosuppressive role via expressing IL1RN. Furthermore, the number of SPP1+ macrophages(SPP1⁺M) significantly increased in tumor microenvirnment, which was correlated with the poor survival of patients. Additionally, regulatory networks based on SPP1⁺M revealed that the disparities of several ligand-receptor pairs existed between tumor and normal tissues. Among these pairs, SPP1-CD44 showed the most interactions between SPP1⁺M and other cell types. Our results provided deep insight into the immune landscape of LUSC and an essential resource for drug discovery in the future.

Online single-cell data integration through projecting heterogeneous datasets into a common cell-embedding space

Computational tools for integrative analyses of diverse single-cell experiments are facing formidable new challenges including dramatic increases in data scale, sample heterogeneity, and the need to informatively cross-reference new data with foundational datasets. Here, we present SCALEX, a deep-learning method that integrates single-cell data by projecting cells into a batch-invariant, common cell-embedding space in a truly online manner (i.e., without retraining the model). SCALEX substantially outperforms online iNMF and other state-of-the-art non-online integration methods on benchmark single-cell datasets of diverse modalities, (e.g., single-cell RNA sequencing, scRNA-seq, single-cell assay for transposase-accessible chromatin use sequencing, scATAC-seq), especially for datasets with partial overlaps, accurately aligning similar cell populations while retaining true biological differences. We showcase SCALEX's advantages by constructing continuously expandable single-cell atlases for human, mouse, and COVID-19 patients, each assembled from diverse data sources and growing with every new data. The online data integration capacity and superior performance makes SCALEX particularly appropriate for large-scale single-cell applications to build upon previous scientific insights.

Single cell RNA-sequencing: A powerful yet still challenging technology to study cellular heterogeneity

Almost all biomedical research to date has relied upon mean measurements from cell populations, however it is well established that what it is observed at this macroscopic level can be the result of many interactions of several different single cells. Thus, the observable macroscopic 'average' cannot outright be used as representative of the 'average cell'. Rather, it is the resulting emerging behaviour of the actions and interactions of many different cells. Single-cell RNA sequencing (scRNA-Seq) enables the comparison of the transcriptomes of individual cells. This provides high-resolution maps of the dynamic cellular programmes allowing us to answer fundamental biological questions on their function and evolution. It also allows to address medical questions such as the role of rare cell populations contributing to disease progression and therapeutic resistance. Furthermore, it provides an understanding of context-specific dependencies, namely the behaviour and function that a cell has in a specific context, which can be crucial to understand some complex diseases, such as diabetes, cardiovascular disease and cancer. Here, we provide an overview of scRNA-Seq, including a comparative review of emerging technologies and computational pipelines. We discuss the current and emerging applications and focus on tumour heterogeneity a clear example of how scRNA-Seq can provide new understanding of a complex disease. Additionally, we review the limitations and highlight the need of powerful computational pipelines and reproducible protocols for the broader acceptance of this technique in basic and clinical research.

Cell Trajectory-Related Genes of Lung Adenocarcinoma Predict Tumor Immune Microenvironment and Prognosis of Patients

Background

Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer which typically exhibits a diverse progression trajectory. Our study sought to explore the cell differentiation trajectory of LUAD and its clinical relevance.

Methods

Utilizing a single-cell RNA-sequencing dataset (GSE117570), we identified LUAD cells of distinct differential status along with differentiation-related genes (DRGs). DRGs were applied to the analysis of bulk-tissue RNA-sequencing dataset (GSE72094) to classify tumors into different subtypes, whose clinical relevance was further analyzed. DRGs were also applied to gene co-expression network analysis (WGCNA) using another bulk-tissue RNA-sequencing dataset (TCGA-LUAD). Genes from modules that demonstrated a significant correlation with clinical traits and were differentially expressed between normal tissue and tumors were identified. Among these, genes with significant prognostic relevance were used for the development of a prognostic nomogram, which was tested on TCGA-LUAD dataset and validated in GSE72094. Finally, CCK-8, EdU, cell apoptosis, cell colony formation, and Transwell assays were used to verify the functions of the identified genes.

Results

Four clusters of cells with distinct differentiation status were characterized, whose DRGs were predominantly correlated with pathways of immune regulation. Based on DRGs, tumors could be clustered into four subtypes associated with distinct immune microenvironment and clinical outcomes. DRGs were categorized into four modules. A total of nine DRGs (SFTPB, WFDC2, HLA-DPA1, TIMP1, MS4A7, HLA-DQA1, VCAN, KRT8, and FABP5) with most significant survival-predicting power were integrated to develop a prognostic model, which outperformed the traditional parameters in predicting clinical outcomes. Finally, we verified that knockdown of WFDC2 inhibited proliferation, migration, and invasion but promoted the apoptosis of A549 cells in vitro.

Conclusion

The cellular composition and cellular differentiation status of tumor mass can predict the clinical outcomes of LUAD patients. It also plays an important role in shaping the tumor immune microenvironment.

Plasticity of monocytes and macrophages in cirrhosis of the liver

Cirrhosis of the liver is a systemic condition with raising prevalence worldwide. Patients with cirrhosis are highly susceptible to develop bacterial infections leading to acute decompensation and acute-on-chronic liver failure both associated with a high morbidity and mortality and sparse therapeutic options other than transplantation. Mononuclear phagocytes play a central role in innate immune responses and represent a first line of defence against pathogens. Their function includes phagocytosis, killing of bacteria, antigen presentation, cytokine production as well as recruitment and activation of immune effector cells. Liver injury and development of cirrhosis induces activation of liver resident Kupffer cells and recruitment of monocytes to the liver. Damage- and pathogen-associated molecular patterns promote systemic inflammation which involves multiple compartments besides the liver, such as the circulation, gut, peritoneal cavity and others. The function of circulating monocytes and tissue macrophages is severely impaired and worsens along with cirrhosis progression. The underlying mechanisms are complex and incompletely understood. Recent 'omics' technologies help to transform our understanding of cellular diversity and function in health and disease. In this review we point out the current state of knowledge on phenotypical and functional changes of monocytes and macrophages during cirrhosis evolution in different compartments and their role in disease progression. We also discuss the value of potential prognostic markers for cirrhosis-associated immuneparesis, and future immunotherapeutic strategies that may reduce the need for transplantation and death.

Endothelial extracellular vesicles promote tumour growth by tumour-associated macrophage reprogramming

Tumour-derived extracellular vesicles (EVs) participate in tumour progression by deregulating various physiological processes including angiogenesis and inflammation. Here we report that EVs released by endothelial cells in a mammary tumour environment participate in the recruitment of macrophages within the tumour, leading to an immunomodulatory phenotype permissive for tumour growth. Using RNA-Seq approaches, we identified several microRNAs (miRNAs) found in endothelial EVs sharing common targets involved in the regulation of the immune system. To further study the impact of these miRNAs in a mouse tumour model, we focused on three miRNAs that are conserved between humans and mouse, that is, miR-142-5p, miR-183-5p and miR-222-3p. These miRNAs are released from endothelial cells in a tumour microenvironment and are transferred via EVs to macrophages. In mouse mammary tumour models, treatment with EVs enriched in these miRNAs leads to a polarization of macrophages toward an M2-like phenotype, which in turn promotes tumour growth.

Proteomic characterization of four subtypes of M2 macrophages derived from human THP-1 cells

Macrophages are widely distributed immune cells that contribute to tissue homeostasis. Human THP-1 cells have been widely used in various macrophage-associated studies, especially those involving pro-inflammatory M1 and anti-inflammatory M2 phenotypes. However, the molecular characterization of four M2 subtypes (M2a, M2b, M2c, and M2d) derived from THP-1 has not been fully investigated. In this study, we systematically analyzed the protein expression profiles of human THP-1-derived macrophages (M0, M1, M2a, M2b, M2c, and M2d) using quantitative proteomics approaches. The commonly and specially regulated proteins of the four M2 subtypes and their potential biological functions were further investigated. The results showed that M2a and M2b, and M2c and M2d have very similar protein expression profiles. These data could serve as an important resource for studies of macrophages using THP-1 cells, and provide a reference to distinguish different M2 subtypes in macrophage-associated diseases for subsequent clinical research.

Integrated analysis of single-cell and bulk RNA sequencing data reveals a pan-cancer stemness signature predicting immunotherapy response

BACKGROUND

Although immune checkpoint inhibitor (ICI) is regarded as a breakthrough in cancer therapy, only a limited fraction of patients benefit from it. Cancer stemness can be the potential culprit in ICI resistance, but direct clinical evidence is lacking.

METHODS

Publicly available scRNA-Seq datasets derived from ICI-treated patients were collected and analyzed to elucidate the association between cancer stemness and ICI response. A novel stemness signature (Stem.Sig) was developed and validated using large-scale pan-cancer data, including 34 scRNA-Seq datasets, The Cancer Genome Atlas (TCGA) pan-cancer cohort, and 10 ICI transcriptomic cohorts. The therapeutic value of Stem.Sig genes was further explored using 17 CRISPR datasets that screened potential immunotherapy targets.

RESULTS

Cancer stemness, as evaluated by CytoTRACE, was found to be significantly associated with ICI resistance in melanoma and basal cell carcinoma (both P < 0.001). Significantly negative association was found between Stem.Sig and anti-tumor immunity, while positive correlations were detected between Stem.Sig and intra-tumoral heterogenicity (ITH) / total mutational burden (TMB). Based on this signature, machine learning model predicted ICI response with an AUC of 0.71 in both validation and testing set. Remarkably, compared with previous well-established signatures, Stem.Sig achieved better predictive performance across multiple cancers. Moreover, we generated a gene list ranked by the average effect of each gene to enhance tumor immune response after genetic knockout across different CRISPR datasets. Then we matched Stem.Sig to this gene list and found Stem.Sig significantly enriched 3% top-ranked genes from the list (P = 0.03), including EMC3, BECN1, VPS35, PCBP2, VPS29, PSMF1, GCLC, KXD1, SPRR1B, PTMA, YBX1, CYP27B1, NACA, PPP1CA, TCEB2, PIGC, NR0B2, PEX13, SERF2, and ZBTB43, which were potential therapeutic targets.

CONCLUSIONS

We revealed a robust link between cancer stemness and immunotherapy resistance and developed a promising signature, Stem.Sig, which showed increased performance in comparison to other signatures regarding ICI response prediction. This signature could serve as a competitive tool for patient selection of immunotherapy. Meanwhile, our study potentially paves the way for overcoming immune resistance by targeting stemness-associated genes.

Oncogenic Mutations and Tumor Microenvironment Alterations of Older Patients With Diffuse Large B-Cell Lymphoma

The incidence of diffuse large B-cell lymphoma (DLBCL) increases by age and older DLBCL are commonly related to poor prognosis. However, the clinical and biological features of older DLBCL patients remain to be determined. A total of 2,445 patients with newly diagnosed DLBCL were enrolled for clinical data analysis according to age at diagnosis, with tumor samples of 1,150 patients assessed by DNA sequencing and 385 patients by RNA sequencing. Older DLBCL presented advanced disease stage, elevated serum lactate dehydrogenase, poor performance status, multiple extranodal involvement, high percentage of double expressor subtype, and adverse clinical outcome. According to molecular features, age was positively correlated with the oncogenic mutations of PIM1, MYD88, BTG2, CD79B, TET2, BTG1, CREBBP, TBL1XR1, and with the MYD88-like genetic subtype. These oncogenic mutations were involved in B-cell receptor/NF-κB signaling, B-cell differentiation, and histone acetylation based on biological functions. Older DLBCL also manifested reduction in CD4⁺ naïve T and CD8⁺ naïve T cells, and also increased recruitment of exhausted T cells and macrophages, leading to immunosuppressive tumor microenvironment. Our work thus contributes to the understanding of aging-related oncogenic mutations and tumor microenvironment alterations in lymphoma progression, and may provide new insights to mechanism-based targeted therapy in DLBCL.

Immunosuppressive TREM2(+) macrophages are associated with undesirable prognosis and responses to anti-PD-1 immunotherapy in non-small cell lung cancer

BACKGROUND

Immune checkpoint blockade (ICB) has been improving patient outcomes of non-small cell lung cancer (NSCLC), but its effectiveness is highly subjective to individual tumor microenvironment. As dominant immune cells in NSCLC, tumor-associated macrophages (TAMs) display high diversity and plasticity. This study aims to find crucial TAM subtypes associated with ICB response in NSCLC.

MATERIALS AND METHODS

Large cohorts of NSCLC patients from The Cancer Genome Atlas and a single-cell sequencing dataset were integrated to illustrate immunosuppressive phenotypes of TAMs, followed by experimental verification. 341 NSCLC surgical samples and 40 tissue samples of NSCLC patients who received ICB treatment were collected to state the clinical importance of TAMs.

RESULTS

We identified a TREM2 positive (⁺) TAM subtype in NSCLC stratifying patient responses to immunotherapy. NSCLC patients with high TREM2⁺ TAM infiltration exhibited advanced tumor progression, inferior prognosis and unique NSCLC molecular characteristics, especially mutations of EGFR. TREM2⁺ TAMs were induced in TME, but not existed in peripheral blood. TREM2⁺ TAMs were enriched with multiple anti-inflammatory cytokines, exhibiting a M2-like immunosuppressive phenotype, and potentiate T cell dysfunction including impaired anti-tumor activity of CD8⁺ T cells and enhanced differentiation towards FOXP3⁺ Tregs, thus facilitating immune evasion of NSCLC. Response rates to PD-1-based ICB were higher in patients with low TREM2⁺ TAMs (31.58%) compared to high TREM2⁺ TAMs (14.29%).

CONCLUSIONS

Our findings implicated the immunosuppressive role of TREM2⁺ TAMs in NSCLC, and systematically reveal the clinical significance of TREM2⁺ TAMs as predictive and prognostic markers for NSCLC patients with ICB treatment.

Investigating the Clinico-Molecular and Immunological Evolution of Lung Adenocarcinoma Using Pseudotime Analysis

Introduction

As the molecular features of lung adenocarcinoma (LUAD) have been evaluated as a cross-sectional study, the course of tumor characteristics has not been modeled. The temporal evolution of the tumor immune microenvironment (TIME), as well as the clinico-molecular features of LUAD, could provide a precise strategy for immunotherapy and surrogate biomarkers for the course of LUAD.

Methods

A pseudotime trajectory was constructed in patients with LUAD from the Cancer Genome Atlas and non-small cell lung cancer radiogenomics datasets. Correlation analyses were performed between clinical features and pseudotime. Genes associated with pseudotime were selected, and gene ontology analysis was performed. F-18 fluorodeoxyglucose positron emission tomography images of subjects were collected, and imaging parameters, including standardized uptake value (SUV), were obtained. Correlation analyses were performed between imaging parameters and pseudotime. Correlation analyses were performed between the enrichment scores of various immune cell types and pseudotime. In addition, correlation analyses were performed between the expression of PD-L1, tumor mutation burden, and pseudotime.

Results

Pseudotime trajectories of LUAD corresponded to clinical stages. Molecular profiles related to cell division and natural killer cell activity were changed along the pseudotime. The maximal SUV of LUAD tumors showed a positive correlation with pseudotime. Type 1 helper T (Th1) cells showed a positive correlation, whereas M2 macrophages showed a negative correlation with pseudotime. PD-L1 expression showed a negative correlation, whereas tumor mutation burden showed a positive correlation with pseudotime.

Conclusion

The estimated pseudotime associated with the stage suggested that it could reflect the clinico-molecular evolution of LUAD. Specific immune cell types in the TIME as well as cell division and glucose metabolism were dynamically changed according to the progression of the pseudotime. As a molecular progression of LUAD, different cellular targets should be considered for immunotherapy.

Spatial Distribution and Predictive Significance of Dendritic Cells and Macrophages in Esophageal Cancer Treated With Combined Chemoradiotherapy and PD-1 Blockade

Background

The first clinical study (NCT03671265) of first-line chemoradiotherapy combined with PD-1 blockade showed promising treatment outcomes in locally advanced esophageal squamous cell carcinoma (ESCC). However, partial patients did not respond to the combination treatment. The roles of dendritic cells (DCs) and macrophages in this combination treatment remain poorly understood.

Methods

We performed multiplexed immunofluorescence method to identify CD11c⁺ DCs, CD68⁺ macrophages, and their PD-L1^- or PD-L1⁺ subpopulations in paired tumor biopsies (n = 36) collected at baseline and during the combination treatment (after radiation, 40 Gy) from the phase Ib trial (NCT03671265). We applied whole exome sequencing in the baseline tumor biopsies (n = 14) to estimate tumor mutation burden (TMB). We dynamically investigated the spatial distribution of DCs and macrophages under chemoradiotherapy combined with PD-1 blockade, and evaluated the association between their spatial distribution and combination outcome, and TMB.

Results

The results showed that high percentages of PD-L1^- DCs and macrophages in the baseline tumor compartment, but not in the stromal compartment, predicted improved OS and PFS. Chemoradiotherapy combined with PD-1 blockade promoted DCs and macrophages to migrate closer to tumor cells. During combination treatment, PD-L1^- tumor cells were nearest to PD-L1^- DCs and macrophages, while PD-L1⁺ tumor cells were next to PD-L1⁺ DCs and macrophages. High TMB was closely associated with a shorter distance from tumor cells to DCs and macrophages. Shorter distance between PD-L1⁺ tumor cells and PD-L1⁺ DCs or PD-L1^- macrophages during the combination was correlated with better OS. Shorter distance between PD-L1^- tumor cells and PD-L1^- macrophages during combination was associated with both longer OS and PFS.

Conclusions

PD-L1^- or PD-L1⁺ DCs and macrophages exhibit distinct spatial distribution in ESCC. The close distance between tumor cells and these antigen-presenting cells (APCs) is critical to the clinical outcome in chemoradiotherapy combined with PD-1 blockade in ESCC patients. Our results highlight the predictive potential of spatial patterns of APCs in chemoradiotherapy combined with immunotherapy and reveal the underlying mechanism of APCs participating in chemoradiotherapy-induced antitumor immune response in ESCC.

Influence of the Metabolism on Myeloid Cell Functions in Cancers: Clinical Perspectives

Tumor metabolism plays a crucial role in sustaining tumorigenesis. There have been increasing reports regarding the role of tumor metabolism in the control of immune cell functions, generating a potent immunosuppressive contexture that can lead to immune escape. The metabolic reprogramming of tumor cells and the immune escape are two major hallmarks of cancer, with several instances of crosstalk between them. In this paper, we review the effects of tumor metabolism on immune cells, focusing on myeloid cells due to their important role in tumorigenesis and immunosuppression from the early stages of the disease. We also discuss ways to target this specific crosstalk in cancer patients.

Identification of a ZC3H12D-regulated competing endogenous RNA network for prognosis of lung adenocarcinoma at single-cell level

Background

To identify hub genes from the competing endogenous RNA (ceRNA) network of lung adenocarcinoma (LUAD) and to explore their potential functions on prognosis of patients from a single-cell perspective.

Methods

We performed RNA-sequencing of LUAD to construct ceRNA regulatory network, integrating with public databases to identify the vital pathways related to patients’ prognosis and to reveal the expression level of hub genes under different conditions, the functional enrichment of co-expressed genes and their potential immune-related mechanisms.

Results

ZC3H12D-hsa-miR-4443-ENST00000630242 axis was found to be related with LUAD. Lower ZC3H12D expression was significantly associated with shorter overall survival (OS) of patients (HR = 2.007, P < 0.05), and its expression was higher in early-stage patients, including T1 (P < 0.05) and N0 (P < 0.05). Additionally, ZC3H12D expression was higher in immune cells displayed by single-cell RNA-sequencing data, especially in Treg cells of lung cancer and CD8 T cells, B cells and CD4 T cells of LUAD. The functional enrichment analysis showed that the co-expressed genes mainly played a role in lymphocyte activation and cytokine-cytokine receptor interaction. In addition, ZC3H12D was associated with multiple immune cells and immune molecules, including immune checkpoints CTLA4, CD96 and TIGIT.

Conclusion

ZC3H12D-hsa-miR-4443-ENST00000630242 ceRNA network was identified in LUAD. ZC3H12D could affect prognosis of patients by regulating mRNA, miRNA, lncRNA, immune cells and immune molecules. Therefore, it may serve as a vital predictive marker and could be regarded as a potential therapeutic target for LUAD in the future.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08992-1.

Evolution and Targeting of Myeloid Suppressor Cells in Cancer: A Translational Perspective

Simple Summary

Immunotherapy is achieving impressive results in the treatment of several cancers. While the main strategies aim to re-invigorate the specific lymphocyte anti-tumor response, many studies underline that altered myeloid cell frequency and functions can dramatically interfere with the responsiveness to cancer therapies. Therefore, many novel strategies targeting TAMs and MDSCs in combination with classical treatments are under continuous evolution at both pre-clinical and clinical levels, showing encouraging results. Herein, we depict a comprehensive overview of myeloid cell generation and function in a cancer setting, and the most relevant strategies for their targeting that are currently in clinical use or under pre-clinical development.

Abstract

In recent years, the immune system has emerged as a critical regulator of tumor development, progression and dissemination. Advanced therapeutic approaches targeting immune cells are currently under clinical use and improvement for the treatment of patients affected by advanced malignancies. Among these, anti-PD1/PD-L1 and anti-CTLA4 immune checkpoint inhibitors (ICIs) are the most effective immunotherapeutic drugs at present. In spite of these advances, great variability in responses to therapy exists among patients, probably due to the heterogeneity of both cancer cells and immune responses, which manifest in diverse forms in the tumor microenvironment (TME). The variability of the immune profile within TME and its prognostic significance largely depend on the frequency of the infiltrating myeloid cells, which often represent the predominant population, characterized by high phenotypic heterogeneity. The generation of heterogeneous myeloid populations endowed with tumor-promoting activities is typically promoted by growing tumors, indicating the sequential levels of myeloid reprogramming as possible antitumor targets. This work reviews the current knowledge on the events governing protumoral myelopoiesis, analyzing the mechanisms that drive the expansion of major myeloid subsets, as well as their functional properties, and highlighting recent translational strategies for clinical developments.

Single-Cell RNA Sequencing in Lung Cancer: Revealing Phenotype Shaping of Stromal Cells in the Microenvironment

The lung tumor microenvironment, which is composed of heterogeneous cell populations, plays an important role in the progression of lung cancer and is closely related to therapeutic efficacy. Increasing evidence has shown that stromal components play a key role in regulating tumor invasion, metastasis and drug resistance. Therefore, a better understanding of stromal components in the tumor microenvironment is helpful for the diagnosis and treatment of lung cancer. Rapid advances in technology have brought our understanding of disease into the genetic era, and single-cell RNA sequencing has enabled us to describe gene expression profiles with unprecedented resolution, enabling quantitative analysis of gene expression at the single-cell level to reveal the correlations among heterogeneity, signaling pathways, drug resistance and microenvironment molding in lung cancer, which is important for the treatment of this disease. In this paper, several common single-cell RNA sequencing methods and their advantages and disadvantages are briefly introduced to provide a reference for selection of suitable methods. Furthermore, we review the latest progress of single-cell RNA sequencing in the study of stromal cells in the lung tumor microenvironment.

Dictionary learning allows model-free pseudotime estimation of transcriptomic data

Background

Pseudotime estimation from dynamic single-cell transcriptomic data enables characterisation and understanding of the underlying processes, for example developmental processes. Various pseudotime estimation methods have been proposed during the last years. Typically, these methods start with a dimension reduction step because the low-dimensional representation is usually easier to analyse. Approaches such as PCA, ICA or t-SNE belong to the most widely used methods for dimension reduction in pseudotime estimation methods. However, these methods usually make assumptions on the derived dimensions, which can result in important dataset properties being missed. In this paper, we suggest a new dictionary learning based approach, dynDLT, for dimension reduction and pseudotime estimation of dynamic transcriptomic data. Dictionary learning is a matrix factorisation approach that does not restrict the dependence of the derived dimensions. To evaluate the performance, we conduct a large simulation study and analyse 8 real-world datasets.

Results

The simulation studies reveal that firstly, dynDLT preserves the simulated patterns in low-dimension and the pseudotimes can be derived from the low-dimensional representation. Secondly, the results show that dynDLT is suitable for the detection of genes exhibiting the simulated dynamic patterns, thereby facilitating the interpretation of the compressed representation and thus the dynamic processes. For the real-world data analysis, we select datasets with samples that are taken at different time points throughout an experiment. The pseudotimes found by dynDLT have high correlations with the experimental times. We compare the results to other approaches used in pseudotime estimation, or those that are method-wise closely connected to dictionary learning: ICA, NMF, PCA, t-SNE, and UMAP. DynDLT has the best overall performance for the simulated and real-world datasets.

Conclusions

We introduce dynDLT, a method that is suitable for pseudotime estimation. Its main advantages are: (1) It presents a model-free approach, meaning that it does not restrict the dependence of the derived dimensions; (2) Genes that are relevant in the detected dynamic processes can be identified from the dictionary matrix; (3) By a restriction of the dictionary entries to positive values, the dictionary atoms are highly interpretable.

Supplementary Information

The online version contains supplementary material available at (10.1186/s12864-021-08276-9).